{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T02:58:45Z","timestamp":1793069925221,"version":"4.5.0"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":42,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"supported by Hunan Provincial Natural Science Foundation of China","award":["2024JJ8032"],"award-info":[{"award-number":["2024JJ8032"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This study proposes a hybrid machine learning framework that integrates one-dimensional convolutional neural networks (1D-CNN) with multi-head attention and Light Gradient Boosting Machines (LightGBM) to model the relationship between physical fitness and body mass index (BMI), thereby generating personalized exercise prescriptions. The dataset consists of 6,698 male students aged 18\u201320 years, including BMI measurements alongside four standardized fitness indicators: 3,000-meter run (aerobic capacity), pull-up test(muscular strength), sit-up test (muscular endurance), and 30\u2009\u00d7\u20092 shuttle run (anaerobic capacity). The 1D-CNN\u2009+\u2009Attention module effectively captures both local and global temporal patterns, while LightGBM significantly enhances classification accuracy through gradient-boosted decision trees. The proposed hybrid architecture achieved state-of-the-art performance in BMI classification, with an accuracy of 94.5% (Cohen\u2019s\n                    <jats:italic>\u03ba<\/jats:italic>\n                    \u2009=\u20090.91) and an F1 score of 0.93, outperforming traditional classifiers by 12.3% to 19.1%. Model interpretability is ensured through SHapley Additive exPlanations (SHAP), which supports dynamic prescription adjustments aimed at improving muscular strength, cardiorespiratory endurance, speed, agility, and flexibility. A 12-week randomized trial demonstrated the clinical efficacy of this framework, yielding a 23.5% reduction in overweight and obesity prevalence, a 15.2% increase in pull-up test performance, and a 9.8% improvement in 30\u2009\u00d7\u20092 shuttle run results. With an inference time of less than 0.8 milliseconds per sample and robust clinical outcomes, this framework provides a scalable real-time solution for data-driven health optimization. It\u2019s well-suited for both clinical and mobile healthcare applications, addressing the growing demand for personalized exercise interventions among young adults.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-42405-2","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T04:53:16Z","timestamp":1773377596000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A machine learning framework for personalized exercise prescription based on BMI and physical fitness assessment"],"prefix":"10.1038","volume":"16","author":[{"given":"Ming","family":"Mo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Buxi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanhong","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianshuo","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guixiang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuyin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"42405_CR1","unstructured":"World Health Organization. Obesity and overweight: Key facts. (2021). https:\/\/www.who.int\/news-room\/factsheets\/detail\/obesity-and-overweight."},{"issue":"16","key":"42405_CR2","doi-asserted-by":"publisher","first-page":"1612","DOI":"10.1001\/jama.2020.1410","volume":"323","author":"JD Smith","year":"2020","unstructured":"Smith, J. D., Fu, E. & Kobayashi, M. A. Prevention and management of childhood obesity and its psychological and health comorbidities. J. Am. Med. Assoc. 323 (16), 1612\u20131614. https:\/\/doi.org\/10.1001\/jama.2020.1410 (2020).","journal-title":"J. Am. Med. Assoc."},{"issue":"12","key":"42405_CR3","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1016\/S2213-8587(18)30288-2","volume":"6","author":"K Bhaskaran","year":"2018","unstructured":"Bhaskaran, K., Dos-Santos-Silva, I., Leon, D. A., Douglas, I. J. & Smeeth, L. Association of BMI with overall and cause-specific mortality: A population-based cohort study of 3.6 million adults in the UK. Lancet Diabetes Endocrinol. 6 (12), 944\u2013953. https:\/\/doi.org\/10.1016\/S2213-8587(18)30288-2 (2018).","journal-title":"Lancet Diabetes Endocrinol."},{"issue":"1","key":"42405_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sj.ijo.0803774","volume":"32","author":"FB Ortega","year":"2008","unstructured":"Ortega, F. B., Ruiz, J. R., Castillo, M. J. & Sj\u00f6str\u00f6m, M. Physical fitness in childhood and adolescence: A powerful marker of health. Int. J. Obes. 32 (1), 1\u201311. https:\/\/doi.org\/10.1038\/sj.ijo.0803774 (2008).","journal-title":"Int. J. Obes."},{"issue":"5","key":"42405_CR5","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1097\/HCO.0000000000000640","volume":"34","author":"DE Warburton","year":"2019","unstructured":"Warburton, D. E. & Bredin, S. S. Health benefits of physical activity: A systematic review of cu rrent systematic reviews. Curr. Opin. 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Med."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-42405-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-42405-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-42405-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T19:56:01Z","timestamp":1781639761000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-42405-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,13]]},"references-count":15,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["42405"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-42405-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-7810535\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,13]]},"assertion":[{"value":"8 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study involved two phases. The model development phase utilized retrospective anonymized physical fitness data from the model-development cohort of 6,698 male students (aged 18\u201320) at Changsha Aeronautical Vocational and Technical College, Hunan province, China, collected from 2020 to 2024. The data, acquired through the institution\u2019s secure platform on April 1, 2024, included anthropometric measurements (height, weight, BMI) and physical fitness test data (3,000-meter run, 30\u2009\u00d7\u20092 shuttle run, pull-up test, sit-up test). A two-stage anonymization protocol was implemented: direct identifiers (name and student ID) were removed at the data collection stage, and numerical perturbation (height\u2009\u00b1\u20090.5\u00a0cm; weight\u2009\u00b1\u20091\u00a0kg) was applied to prevent re-identification. The intervention validation phase was a prospective randomized controlled trial with the intervention cohort of 1,160 participants approved by the Research Office of Changsha Aeronautical Vocational and Technical College. Informed consent was obtained from all participants in the trial. The study was conducted in accordance with the\n                      Declaration of Helsinki\n                      . Informed consent was obtained from all participants, and ethical approval was granted by the Human Ethics Committee of Chizhou University (Approval No.: 202301015).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics statement"}},{"value":"This study adheres to the CONSORT (Consolidated Standards of Reporting Trials) checklist (for non-randomized controlled trials).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Reporting guidelines"}}],"article-number":"13336"}}